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This paper presents a health analysis technique for transformer winding insulation through thermal monitoring and Fast Fourier Transform (FFT) power spectrum. A novel thermal model for the Kraft paper insulation of transformer is proposed by using the transformer's top-oil and winding hot-spot temperature models. The relationship between the temperature rise of oil inside the transformer tank and the winding insulation degradation are considered by utilizing the data-sets and daily load cycles of a 10/13 MVA, 132/11 kV, 50 Hz, ONAF grid power transformer. The model based on IEEE Guide for loading mineral-oil-immersed transformers is developed in Simulink. The hotspot temperature rise from the thermal model is used as a reference to analyze the winding insulation degradation in the form of high frequency partial discharges (PDs) upon the output parameters of the transformer. Using data analysis techniques, a correlation is presented between the load cycles and the hot-spot temperature through which the health status of the transformer winding insulation is estimated. Moreover, the high frequency transients were detected using the Fast Fourier Transform (FFT) spectrum analyzer tool in MATLAB. The preliminary study shows that high frequency PDs are detected for the overheated and deteriorated state of the winding insulation. The results show that the proposed technique is feasible for the health analysis of power transformers and successfully predicted the deterioration of the transformer winding insulation.

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